This document analyzes a dataset of chess games from Lichess provided by #TidyTuesday. It contains over 20,000 games, including information such as player ratings, move sequences, and other metrics. The main focus of this analysis will be on the specific moves played during each game.
Setting up
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set.seed(321) # For reproducibility# Loading packages -------------------------------------------------------------pacman::p_load( tidyverse, # Easily Install and Load the 'Tidyverse' rchess, # Working with chess PGN notation ggtext, # Improved Text Rendering Support for 'ggplot2' showtext, # Using Fonts More Easily in R Graphs ggeasy, # Makes theming plots easier furrr, # Parallel processing glue, # Interpreted String Literals ggfx # Pixel Filters for "ggplot2" and "grid")# Visualization Parameters -----------------------------------------------------# Plot aestheticstitle_col <-"gray20"subtitle_col <-"gray20"caption_col <-"gray30"text_col <-"gray20"col_palette <- paletteer::paletteer_d("peRReo::don")[c(5,9)]chess_tiles <-c("burlywood3", "burlywood4") # Traditional board colours# Iconstt <-str_glue("#TidyTuesday: { 2024 } Week { 40 } • Source: Chess Game Dataset (Lichess)<br>")li <-str_glue("<span style='font-family:fa6-brands'></span>")gh <-str_glue("<span style='font-family:fa6-brands'></span>")# Texttitle_text <-str_glue("Positional movements of major chess pieces")caption_text <-str_glue("{tt} {li} c-monaghan • {gh} c-monaghan • #rstats #ggplot2")# Fontsfont_add("fa6-brands", here::here("fonts/6.4.2/Font Awesome 6 Brands-Regular-400.otf"))font_add_google("Oswald", regular.wt =400, family ="title")font_add_google("Noto Sans", regular.wt =400, family ="caption")showtext_auto(enable =TRUE)# Plot themetheme_set(theme_void(base_size =14, base_family ="text"))theme_update(plot.title.position ="plot",plot.caption.position ="plot",legend.position ='plot',panel.grid =element_blank(),strip.text =element_text(size =20, face ="bold", margin =margin(0, 0, 2, 0)),axis.text =element_blank(),axis.ticks =element_blank())# Variables --------------------------------------------------------------------# Pathspath <-"posts/2024/"folder <-"2024-09-28-TT-W40/"# White Pieceswhite_pieces <-c("a1 Rook", "b1 Knight", "c1 Bishop", "White Queen","White King", "f1 Bishop", "g1 Knight", "h1 Rook")# Reading in data --------------------------------------------------------------data <- tidytuesdayR::tt_load(2024, week =40)data <- data$chess
Chess Data
The dataset includes the move sequences made by each player, represented as a string of moves. Below is a preview of the first two rows of the dataset.
Each row displays the move sequence for a game, starting with White’s move. For example, the Slav Defense (1. d4 d5 2. c4 c6) or the Nimzowitsch Defense (1. d4 Nc6 2. e4 e5). Chess moves are typically recorded using Portable Game Notation (PGN), which makes it easy to replicate games.
PGN Conversion
To facilitate further analysis, we will convert the raw move strings into the PGN format, using a custom convert_to_pgn() function that I wrote.
Show the code
# Convert moves into PGN format ------------------------------------------------convert_to_pgn <-function(moves, game_id) {# Split the moves string into a list of individual moves move_list <-strsplit(moves, " ")[[1]]# Initialize an empty string for the PGN format pgn <-""# Loop through the moves two at a time (each move is a pair: white and black)for (i inseq(1, length(move_list), by =2)) { move_number <- (i +1) /2# Move number calculation# Add both white's and black's moves, if availableif (i <length(move_list)) { pgn <-paste0(pgn, move_number, ". ", move_list[i], " ", move_list[i+1], " ") } else { # In case the game ends on white's move (no black move) pgn <-paste0(pgn, move_number, ". ", move_list[i]) } }# Returning pgn stringreturn(pgn)}# Converting moves into pgn formatchess_games <- data %>%select(game_id, moves) %>%mutate(game_id =seq(1:nrow(data)),moves =mapply(convert_to_pgn, moves, game_id))# Displaying converted datasetchess_games %>%rename(ID = game_id, Moves = moves) %>%head(n =2)
Even with parallel processing the below code takes extremely long to run when rendering in quarto (4+ hrs). In a normal R session it takes about 20 minutes. I am not quite sure as to why the time difference is so extreme. However, to make my life a little easier and so I’m not waiting for hours when I need to re-render this document I will be reading in the data that this code outputs from an excel file.
However, for those interested I have left the parallel processing code below
Game history code
Now that the data is in PGN format, we can use the history_detail() function from the rhcess package to extract the game history. However, because the dataset contains over 20,000 rows, processing every game sequentially would be time-consuming. To speed up this process, we will implement parallel processing using the furrr package.
Show the code
# Function to extract game historyprocess_moves <-function(p) { chss <- Chess$new() # Initialize a new chess object chss$load_pgn(p) # Load the PGN notation into the chess object chss$history_detail() # Extract detailed history of the game}plan(multisession, workers = parallel::detectCores() -1)# Parallel processing: Convert PGN format to game historychess_games <- chess_games %>%mutate(data =future_map(moves, process_moves)) %>%select(-moves) %>%unnest(cols =c(data))
The game history data contains 1,393,586 observations on both white and black’s moves. For the purpose of analysis we are only interested in plotting white’s moves. Additionally, we will sample 25,000 moves from white so as not to create an over-populated graph.
# A tibble: 4 × 4
game_id piece from to
<dbl> <chr> <chr> <chr>
1 7298 b1 Knight b1 d2
2 16254 g1 Knight e5 c4
3 19492 h1 Rook h1 e1
4 10827 White Queen b6 e3
Positional Movements
Creating a chess board
First, we will create a chessboard using ggplot. We can do this using the .chessboard() function, again from the rchess package. This function generates the grid and coordinates for each square which can piped into ggplot.
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# Creating a chess board -------------------------------------------------------board <- rchess:::.chessboarddata() %>%tibble() %>%select(cell, col, row, x, y, cc)board %>%head(n =4)
# A tibble: 4 × 6
cell col row x y cc
<chr> <chr> <int> <int> <int> <chr>
1 a1 a 1 1 1 b
2 b1 b 1 2 1 w
3 c1 c 1 3 1 b
4 d1 d 1 4 1 w
Adding board movements
Next, we will join the chessboard data with the game data to calculate the origin and destination of each piece movement. This allows us to visualize the trajectories of pieces as they move across the board.
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# Join board data with game data to get move origin and destination chess_games <- chess_games %>%left_join(board %>%rename(from = cell, x.from = x, y.from = y), by ="from") %>%left_join(board %>%rename(to = cell, x.to = x, y.to = y) %>%select(-cc, -col, -row), # Exclude unnecessary columnsby ="to") %>%mutate(x_gt_y =abs(x.to - x.from) >abs(y.to - y.from), # Check if x movement is greater than yxy_sign =sign((x.to - x.from)*(y.to - y.from)) ==1, # Check if X and Y move in the same directionx_gt_y_equal_xy_sign = x_gt_y == xy_sign) # Check if both conditions hold# Previewingchess_games %>%select(piece, from, to, x.from, y.from, x.to, y.to) %>%head(n =4)
---title: "Visualising positional moves in chess"subtitle: "Where do players move their chess pieces to"date: "2024-09-28"format: html: page-layout: full html-math-method: katexcategories: - "#TidyTuesday" - data visualisationimage: "../thumbnails/tt_2024_w40_thumb.png"citation: url: "https://c-monaghan.github.io/posts/2024/2024-09-28-TT-W40/"execute: warning: falseeditor: source---{#fig-position .lightbox}This document analyzes a dataset of chess games from [Lichess](https://lichess.org/) provided by [#TidyTuesday](https://github.com/rfordatascience/tidytuesday). It contains over 20,000 games, including information such as player ratings, move sequences, and other metrics. The main focus of this analysis will be on the **specific moves played during each game**.# Setting up```{r setting-up, output = FALSE}set.seed(321) # For reproducibility# Loading packages -------------------------------------------------------------pacman::p_load( tidyverse, # Easily Install and Load the 'Tidyverse' rchess, # Working with chess PGN notation ggtext, # Improved Text Rendering Support for 'ggplot2' showtext, # Using Fonts More Easily in R Graphs ggeasy, # Makes theming plots easier furrr, # Parallel processing glue, # Interpreted String Literals ggfx # Pixel Filters for "ggplot2" and "grid")# Visualization Parameters -----------------------------------------------------# Plot aestheticstitle_col <- "gray20" subtitle_col <- "gray20" caption_col <- "gray30" text_col <- "gray20" col_palette <- paletteer::paletteer_d("peRReo::don")[c(5,9)]chess_tiles <- c("burlywood3", "burlywood4") # Traditional board colours# Iconstt <- str_glue("#TidyTuesday: { 2024 } Week { 40 } • Source: Chess Game Dataset (Lichess)<br>")li <- str_glue("<span style='font-family:fa6-brands'></span>")gh <- str_glue("<span style='font-family:fa6-brands'></span>")# Texttitle_text <- str_glue("Positional movements of major chess pieces")caption_text <- str_glue("{tt} {li} c-monaghan • {gh} c-monaghan • #rstats #ggplot2")# Fontsfont_add("fa6-brands", here::here("fonts/6.4.2/Font Awesome 6 Brands-Regular-400.otf"))font_add_google("Oswald", regular.wt = 400, family = "title")font_add_google("Noto Sans", regular.wt = 400, family = "caption")showtext_auto(enable = TRUE)# Plot themetheme_set(theme_void(base_size = 14, base_family = "text"))theme_update( plot.title.position = "plot", plot.caption.position = "plot", legend.position = 'plot', panel.grid = element_blank(), strip.text = element_text(size = 20, face = "bold", margin = margin(0, 0, 2, 0)), axis.text = element_blank(), axis.ticks = element_blank())# Variables --------------------------------------------------------------------# Pathspath <- "posts/2024/"folder <- "2024-09-28-TT-W40/"# White Pieceswhite_pieces <- c("a1 Rook", "b1 Knight", "c1 Bishop", "White Queen", "White King", "f1 Bishop", "g1 Knight", "h1 Rook")# Reading in data --------------------------------------------------------------data <- tidytuesdayR::tt_load(2024, week = 40)data <- data$chess```# Chess DataThe dataset includes the move sequences made by each player, represented as a string of moves. Below is a preview of the first two rows of the dataset.```{r data-moves}data %>% select(game_id, moves) %>% mutate(game_id = seq(1:nrow(data))) %>% rename(ID = game_id, Moves = moves) %>% head(n = 2)```Each row displays the move sequence for a game, starting with White's move. For example, the **Slav Defense** (1. d4 d5 2. c4 c6) or the **Nimzowitsch Defense** (1. d4 Nc6 2. e4 e5). Chess moves are typically recorded using [Portable Game Notation (PGN)](https://www.chess.com/terms/chess-pgn), which makes it easy to replicate games. ## PGN ConversionTo facilitate further analysis, we will convert the raw move strings into the PGN format, using a custom `convert_to_pgn()` function that I wrote.```{r convert-to-pgn}# Convert moves into PGN format ------------------------------------------------convert_to_pgn <- function(moves, game_id) { # Split the moves string into a list of individual moves move_list <- strsplit(moves, " ")[[1]] # Initialize an empty string for the PGN format pgn <- "" # Loop through the moves two at a time (each move is a pair: white and black) for (i in seq(1, length(move_list), by = 2)) { move_number <- (i + 1) / 2 # Move number calculation # Add both white's and black's moves, if available if (i < length(move_list)) { pgn <- paste0(pgn, move_number, ". ", move_list[i], " ", move_list[i+1], " ") } else { # In case the game ends on white's move (no black move) pgn <- paste0(pgn, move_number, ". ", move_list[i]) } } # Returning pgn string return(pgn)}# Converting moves into pgn formatchess_games <- data %>% select(game_id, moves) %>% mutate( game_id = seq(1:nrow(data)), moves = mapply(convert_to_pgn, moves, game_id))# Displaying converted datasetchess_games %>% rename(ID = game_id, Moves = moves) %>% head(n = 2)```## Extracting game historyEven with parallel processing the below code takes extremely long to run when rendering in quarto (4+ hrs). In a normal R session it takes about 20 minutes. I am not quite sure as to why the time difference is so extreme. However, to make my life a little easier and so I'm not waiting for hours when I need to re-render this document I will be reading in the data that this code outputs from an excel file. However, for those interested I have left the parallel processing code below::: {.callout-note collapse="true}## Game history codeNow that the data is in PGN format, we can use the `history_detail()` function from the `rhcess` package to extract the game history. However, because the dataset contains over 20,000 rows, processing every game sequentially would be time-consuming. To speed up this process, we will implement parallel processing using the `furrr` package.```{r game-history, eval=FALSE}# Function to extract game historyprocess_moves <- function(p) { chss <- Chess$new() # Initialize a new chess object chss$load_pgn(p) # Load the PGN notation into the chess object chss$history_detail() # Extract detailed history of the game}plan(multisession, workers = parallel::detectCores() - 1)# Parallel processing: Convert PGN format to game historychess_games <- chess_games %>% mutate(data = future_map(moves, process_moves)) %>% select(-moves) %>% unnest(cols = c(data))```The game history data contains 1,393,586 observations on both white and black's moves. For the purpose of analysis we are only interested in plotting white's moves. Additionally, we will sample 25,000 moves from white so as not to create an over-populated graph.```{r filtering-to-white, eval=FALSE}# White pieceswhite_pieces <- c( "a1 Rook", "b1 Knight", "c1 Bishop", "White Queen", "White King", "f1 Bishop", "g1 Knight", "h1 Rook")chess_games <- chess_games %>% filter(piece %in% white_pieces) %>% sample_n(25000)```:::```{r history-data}chess_games <- readxl::read_xlsx(path = here::here("data/chess_history.xlsx"))chess_games %>% select(game_id, piece, from, to) %>% head(n = 4)```# Positional Movements## Creating a chess boardFirst, we will create a chessboard using `ggplot`. We can do this using the `.chessboard()` function, again from the `rchess` package. This function generates the grid and coordinates for each square which can piped into ggplot.```{r chess-board}# Creating a chess board -------------------------------------------------------board <- rchess:::.chessboarddata() %>% tibble() %>% select(cell, col, row, x, y, cc)board %>% head(n = 4)```## Adding board movementsNext, we will join the chessboard data with the game data to calculate the origin and destination of each piece movement. This allows us to visualize the trajectories of pieces as they move across the board.```{r board-movements}# Join board data with game data to get move origin and destination chess_games <- chess_games %>% left_join(board %>% rename(from = cell, x.from = x, y.from = y), by = "from") %>% left_join(board %>% rename(to = cell, x.to = x, y.to = y) %>% select(-cc, -col, -row), # Exclude unnecessary columns by = "to") %>% mutate(x_gt_y = abs(x.to - x.from) > abs(y.to - y.from), # Check if x movement is greater than y xy_sign = sign((x.to - x.from)*(y.to - y.from)) == 1, # Check if X and Y move in the same direction x_gt_y_equal_xy_sign = x_gt_y == xy_sign) # Check if both conditions hold# Previewingchess_games %>% select(piece, from, to, x.from, y.from, x.to, y.to) %>% head(n = 4)```## Plotting piece movementsNow we can visualize their paths on a chessboard.```{r piece-movement}#| fig-align: center#| fig-width: 7.5#| fig-height: 5#| fig-cap: Positional movements of major white chess pieces. Denser curves indicate that pieces are move often moved in that direction.p <- chess_games %>% ggplot() + # Adding board data geom_tile(data = board, aes(x, y, fill = cc)) + # Adding piece movement geom_curve( data = chess_games %>% filter(x_gt_y_equal_xy_sign), aes(x = x.from, y = y.from, xend = x.to, yend = y.to), position = position_jitter(width = 0.2, height = 0.2), curvature = 0.50, angle = -45, alpha = 0.02, color = "white", linewidth = 1.02) + geom_curve( data = chess_games %>% filter(!x_gt_y_equal_xy_sign), aes(x = x.from, y = y.from, xend = x.to, yend = y.to), position = position_jitter(width = 0.2, height = 0.2), curvature = -0.50, angle = 45, alpha = 0.02, color = "white", linewidth = 1.02) + # Labs labs( title = title_text, caption = caption_text, x = NULL, y = NULL) + # Scales scale_fill_manual(values = chess_tiles) + coord_equal() + # Facet facet_wrap(~ factor(piece, levels = white_pieces), ncol = 4) + # Theme theme( plot.title = element_text( size = 50, family = "title", face = "bold", colour = title_col, lineheight = 1.1, hjust = 0.5, margin = margin(t = 5, b = 5)), plot.caption = element_markdown( size = rel(1.25), family = "caption", colour = caption_col, lineheight = 1.1, hjust = 0.5, margin = margin(t = 5, b = 5)) )p```# Saving```{r save-plot}# Saving plotggsave( filename = here::here(path, folder, "tt_2024_w40.png"), plot = p, width = 7.5, height = 5, units = "in", dpi = 320)# Thumbnail magick::image_read(here::here(path, folder, "tt_2024_w40.png")) |> magick::image_resize(geometry = "800") |> magick::image_write(here::here(path, "thumbnails/tt_2024_w40_thumb.png"))``````{r}#| echo: false#| child: "/Users/corma/OneDrive/Desktop/C-Monaghan.github.io/scripts/appendix.qmd"```
